Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.
One fundamental challenge of autonomous driving is efficiently, accurately, and in real-time, determining the location of an autonomous driving vehicle (ADV) with respect to a high definition (HD) map having various crucial information annotated. In a worst case, accuracy needs to be within 10 cm. ADV position in the high definition (HD) map is used by ADV system components such as perception, planning, and control, to make precise and timely ADV driving decisions. To determine a position of the ADV within the HD map, one or more ADV position sensors are included in, or on, the ADV. Sensors can include a global positioning satellite detector (GPS), inertial measurement unit sensor (IMU), radio detection and ranging (RADAR) and light detection and ranging (LIDAR). Existing hardware-based positioning systems, such as global positioning satellite sensor (GPS) and inertial measurement unit sensor (IMU) cannot provide the necessary accuracy with respect to the HD map, especially for dynamic urban environment having complex signal occlusion situations.
Existing localization methods for autonomous driving vehicles typically are of three major categories: 2D, 3D and 2D-3D fused methods. Among these three, 3D based methods using laser scanner (e.g. LIDAR sensor) is currently popular due to its high accuracy and reliability. Prior art methods using a LIDAR sensor to determine ADV position in an HD map are computationally expensive and have only modest accuracy and modest robustness.